The invention relates to genetic algorithms, and to the use of genetic algorithms in designing neural networks (NNs).
One aspect of NN design is the specification of the interconnections among a NN's neurons to achieve a desired input-output (I/O) relationship. In particular, for the class of long term memory (LTM) time invariant NNs, the NN is programmed for a particular I/O relationship by choosing the weight of each interconnecting trace between a pair of neurons.
Conventional NN design techniques make simplifying assumptions so that the problem of determining the trace weights is tractable. Examples of these simplifying assumptions are that the neurons are arranged in layers within which no lateral connections are made or that a neuron can be simultaneously excitatory and inhibitory, i.e., the weights of traces from a particular neuron to others in the NN can be positive to some and negative to others.